Microscopic examination of stained and processed tissue is the cornerstone of disease diagnosis in the field of histopathology. For the purpose of diagnosis, the biopsied or resected tissues obtained by surgical procedures are processed, stained, mounted on glass slides and viewed under microscopes, conventionally. By automating this process, digital pathology adds the much-needed speed and accuracy to the conventional method of glass slide based histopathology.
Digital pathology is a process of converting glass microscopy slides into high-resolution digital images. These images can be viewed, managed, analyzed and interpreted with a computer-based digital pathology work flow management system, instead of a microscope. Digital pathology converts the conventional glass slide based process into a dynamic digital image based one. This process allows faster and more accurate analysis and reporting, easy archival and retrieval of stored images and metadata, and facilitates transfer of digitized slides over shared networks for consultations, second opinions, education and quality control.
For the scanning process to be efficient in terms of speed and storage space, the tissue on the glass slide needs to be accurately identified and differentiated from area on the glass slide not covered by tissue. The identification of this tissue area is also described as detection of AOI or area of interest. A thumbnail image of the slide generated by a low-resolution camera is used for the detection of the area of interest. Automated detection of the AOI in the thumbnail image can be performed using different techniques.
Prior arts detailed as follows exist for thumbnail area of interest (AOI) detection methods:
U.S. Pat. No. 8,565,553 describes a method for thumbnail AOI (area of interest) detection through image segmentation technique based on threshold values and additional parameters derived from empirical data.
U.S. Pat. No. 7,869,641 describes a system and method for finding regions of interest for microscopic digital montage imaging using a standard microscope and a camera.
However, there remains a need for an algorithm that addresses the following issues:
Detection of the foreground (tissue) pixels from the background pixels even when the thumbnail image has uneven illumination.
Detection of tissue areas even when the staining intensity is not optimum.
Detection of tissue areas stained by various staining methods applying appropriate stain specific artifact filters.
The present invention based on segmentation techniques seeks to address all these issues.